Synapse location during growth depends on glia location.

Synaptic contacts are largely established during embryogenesis and are then maintained during growth. To identify molecules involved in this process, we conducted a forward genetic screen in C. elegans and identified cima-1. In cima-1 mutants, synaptic contacts are correctly established during embryogenesis, but ectopic synapses emerge during postdevelopmental growth. cima-1 encodes a solute carrier in the SLC17 family of transporters that includes sialin, a protein that when mutated in humans results in neurological disorders. cima-1 does not function in neurons but rather functions in the nearby epidermal cells to correctly position glia during postlarval growth. Our findings indicate that CIMA-1 antagonizes the FGF receptor (FGFR), and does so most likely by inhibiting FGFR's role in epidermal-glia adhesion rather than signaling. Our data suggest that epidermal-glia crosstalk, in this case mediated by a transporter and the FGF receptor, is vital to preserve embryonically derived circuit architecture during postdevelopmental growth.

Multiview confocal super-resolution microscopy.

Confocal microscopy1 remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, but suffers from substantial point spread function anisotropy, diffraction-limited resolution, depth-dependent degradation in scattering samples and volumetric bleaching2. Here we address these problems, enhancing confocal microscopy performance from the sub-micrometre to millimetre spatial scale and the millisecond to hour temporal scale, improving both lateral and axial resolution more than twofold while simultaneously reducing phototoxicity. We achieve these gains using an integrated, four-pronged approach: (1) developing compact line scanners that enable sensitive, rapid, diffraction-limited imaging over large areas; (2) combining line-scanning with multiview imaging, developing reconstruction algorithms that improve resolution isotropy and recover signal otherwise lost to scattering; (3) adapting techniques from structured illumination microscopy, achieving super-resolution imaging in densely labelled, thick samples; (4) synergizing deep learning with these advances, further improving imaging speed, resolution and duration. We demonstrate these capabilities on more than 20 distinct fixed and live samples, including protein distributions in single cells; nuclei and developing neurons in Caenorhabditis elegans embryos, larvae and adults; myoblasts in imaginal disks of Drosophila wings; and mouse renal, oesophageal, cardiac and brain tissues.

Structural and developmental principles of neuropil assembly in C. elegans.

Neuropil is a fundamental form of tissue organization within the brain1, in which densely packed neurons synaptically interconnect into precise circuit architecture2,3. However, the structural and developmental principles that govern this nanoscale precision remain largely unknown4,5. Here we use an iterative data coarse-graining algorithm termed 'diffusion condensation'6 to identify nested circuit structures within the Caenorhabditis elegans neuropil, which is known as the nerve ring. We show that the nerve ring neuropil is largely organized into four strata that are composed of related behavioural circuits. The stratified architecture of the neuropil is a geometrical representation of the functional segregation of sensory information and motor outputs, with specific sensory organs and muscle quadrants mapping onto particular neuropil strata. We identify groups of neurons with unique morphologies that integrate information across strata and that create neural structures that cage the strata within the nerve ring. We use high resolution light-sheet microscopy7,8 coupled with lineage-tracing and cell-tracking algorithms9,10 to resolve the developmental sequence and reveal principles of cell position, migration and outgrowth that guide stratified neuropil organization. Our results uncover conserved structural design principles that underlie the architecture and function of the nerve ring neuropil, and reveal a temporal progression of outgrowth-based on pioneer neurons-that guides the hierarchical development of the layered neuropil. Our findings provide a systematic blueprint for using structural and developmental approaches to understand neuropil organization within the brain.

Incorporating the image formation process into deep learning improves network performance.

We present Richardson-Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson-Lucy iteration with a fully convolutional network structure, establishing a connection to the image formation process and thereby improving network performance. Containing only roughly 16,000 parameters, RLN enables four- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms classic Richardson-Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides four- to sixfold faster reconstructions of large, cleared-tissue datasets than classic multi-view pipelines. We demonstrate RLN's performance on cells, tissues and embryos imaged with widefield-, light-sheet-, confocal- and super-resolution microscopy.

Analysis of homeodomain specificities allows the family-wide prediction of preferred recognition sites.

We describe the comprehensive characterization of homeodomain DNA-binding specificities from a metazoan genome. The analysis of all 84 independent homeodomains from D. melanogaster reveals the breadth of DNA sequences that can be specified by this recognition motif. The majority of these factors can be organized into 11 different specificity groups, where the preferred recognition sequence between these groups can differ at up to four of the six core recognition positions. Analysis of the recognition motifs within these groups led to a catalog of common specificity determinants that may cooperate or compete to define the binding site preference. With these recognition principles, a homeodomain can be reengineered to create factors where its specificity is altered at the majority of recognition positions. This resource also allows prediction of homeodomain specificities from other organisms, which is demonstrated by the prediction and analysis of human homeodomain specificities.

Multi-tissue patterning drives anterior morphogenesis of the C. elegans embryo.

Complex structures derived from multiple tissue types are challenging to study in vivo, and our knowledge of how cells from different tissues are coordinated is limited. Model organisms have proven invaluable for improving our understanding of how chemical and mechanical cues between cells from two different tissues can govern specific morphogenetic events. Here we used Caenorhabditis elegans as a model system to show how cells from three different tissues are coordinated to give rise to the anterior lumen. While some aspects of pharyngeal morphogenesis have been well-described, it is less clear how cells from the pharynx, epidermis and neuroblasts coordinate to define the location of the anterior lumen and supporting structures. Using various microscopy and software approaches, we define the movements and patterns of these cells during anterior morphogenesis. Projections from the anterior-most pharyngeal cells (arcade cells) provide the first visible markers for the location of the future lumen, and facilitate patterning of the surrounding neuroblasts. These neuroblast patterns control the rate of migration of the anterior epidermal cells, whereas the epidermal cells ultimately reinforce and control the position of the future lumen, as they must join with the pharyngeal cells for their epithelialization. Our studies are the first to characterize anterior morphogenesis in C. elegans in detail and should lay the framework for identifying how these different patterns are controlled at the molecular level.

Adaptive optics improves multiphoton super-resolution imaging.

We improve multiphoton structured illumination microscopy using a nonlinear guide star to determine optical aberrations and a deformable mirror to correct them. We demonstrate our method on bead phantoms, cells in collagen gels, nematode larvae and embryos, Drosophila brain, and zebrafish embryos. Peak intensity is increased (up to 40-fold) and resolution recovered (up to 176 ± 10 nm laterally, 729 ± 39 nm axially) at depths ∼250 µm from the coverslip surface.

Pediatric toxic epidermal necrolysis treated successfully with infliximab.

Successful management of toxic epidermal necrolysis (TEN) with tumor necrosis factor-α inhibitors has been described in adults. We present a case of a 7-year-old boy with infection-associated TEN, diagnosed by typical clinical and histopathological features, most likely caused by Mycoplasma pneumoniae. Treatment with a single dose of infliximab 5 mg/kg intravenously on day 5 after the onset of symptoms was followed by cessation of all blister formation over 3 days and complete resolution within a week. Sequelae were mild, consisting of postinflammatory hyperpigmentation and dry eyes.

Visualizing Calcium Flux in Freely Moving Nematode Embryos.

The lack of physiological recordings from Caenorhabditis elegans embryos stands in stark contrast to the comprehensive anatomical and gene expression datasets already available. Using light-sheet fluorescence microscopy to address the challenges associated with functional imaging at this developmental stage, we recorded calcium dynamics in muscles and neurons and developed analysis strategies to relate activity and movement. In muscles, we found that the initiation of twitching was associated with a spreading calcium wave in a dorsal muscle bundle. Correlated activity in muscle bundles was linked with early twitching and eventual coordinated movement. To identify neuronal correlates of behavior, we monitored brainwide activity with subcellular resolution and identified a particularly active cell associated with muscle contractions. Finally, imaging neurons of a well-defined adult motor circuit, we found that reversals in the eggshell correlated with calcium transients in AVA interneurons.

Global analysis of Drosophila Cys2-His2 zinc finger proteins reveals a multitude of novel recognition motifs and binding determinants.

Cys2-His2 zinc finger proteins (ZFPs) are the largest group of transcription factors in higher metazoans. A complete characterization of these ZFPs and their associated target sequences is pivotal to fully annotate transcriptional regulatory networks in metazoan genomes. As a first step in this process, we have characterized the DNA-binding specificities of 129 zinc finger sets from Drosophila using a bacterial one-hybrid system. This data set contains the DNA-binding specificities for at least one encoded ZFP from 70 unique genes and 23 alternate splice isoforms representing the largest set of characterized ZFPs from any organism described to date. These recognition motifs can be used to predict genomic binding sites for these factors within the fruit fly genome. Subsets of fingers from these ZFPs were characterized to define their orientation and register on their recognition sequences, thereby allowing us to define the recognition diversity within this finger set. We find that the characterized fingers can specify 47 of the 64 possible DNA triplets. To confirm the utility of our finger recognition models, we employed subsets of Drosophila fingers in combination with an existing archive of artificial zinc finger modules to create ZFPs with novel DNA-binding specificity. These hybrids of natural and artificial fingers can be used to create functional zinc finger nucleases for editing vertebrate genomes.

An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins.

Cys(2)-His(2) zinc finger proteins (ZFPs) are the largest family of transcription factors in higher metazoans. They also represent the most diverse family with regards to the composition of their recognition sequences. Although there are a number of ZFPs with characterized DNA-binding preferences, the specificity of the vast majority of ZFPs is unknown and cannot be directly inferred by homology due to the diversity of recognition residues present within individual fingers. Given the large number of unique zinc fingers and assemblies present across eukaryotes, a comprehensive predictive recognition model that could accurately estimate the DNA-binding specificity of any ZFP based on its amino acid sequence would have great utility. Toward this goal, we have used the DNA-binding specificities of 678 two-finger modules from both natural and artificial sources to construct a random forest-based predictive model for ZFP recognition. We find that our recognition model outperforms previously described determinant-based recognition models for ZFPs, and can successfully estimate the specificity of naturally occurring ZFPs with previously defined specificities.

Exploring the DNA-recognition potential of homeodomains.

The recognition potential of most families of DNA-binding domains (DBDs) remains relatively unexplored. Homeodomains (HDs), like many other families of DBDs, display limited diversity in their preferred recognition sequences. To explore the recognition potential of HDs, we utilized a bacterial selection system to isolate HD variants, from a randomized library, that are compatible with each of the 64 possible 3' triplet sites (i.e., TAANNN). The majority of these selections yielded sets of HDs with overrepresented residues at specific recognition positions, implying the selection of specific binders. The DNA-binding specificity of 151 representative HD variants was subsequently characterized, identifying HDs that preferentially recognize 44 of these target sites. Many of these variants contain novel combinations of specificity determinants that are uncommon or absent in extant HDs. These novel determinants, when grafted into different HD backbones, produce a corresponding alteration in specificity. This information was used to create more explicit HD recognition models, which can inform the prediction of transcriptional regulatory networks for extant HDs or the engineering of HDs with novel DNA-recognition potential. The diversity of recovered HD recognition sequences raises important questions about the fitness barrier that restricts the evolution of alternate recognition modalities in natural systems.

An optimized two-finger archive for ZFN-mediated gene targeting.

The widespread use of zinc-finger nucleases (ZFNs) for genome engineering is hampered by the fact that only a subset of sequences can be efficiently recognized using published finger archives. We describe a set of validated two-finger modules that complement existing finger archives and expand the range of ZFN-accessible sequences threefold. Using this archive, we introduced lesions at 9 of 11 target sites in the zebrafish genome.

Using defined finger-finger interfaces as units of assembly for constructing zinc-finger nucleases.

Zinc-finger nucleases (ZFNs) have been used for genome engineering in a wide variety of organisms; however, it remains challenging to design effective ZFNs for many genomic sequences using publicly available zinc-finger modules. This limitation is in part because of potential finger-finger incompatibility generated on assembly of modules into zinc-finger arrays (ZFAs). Herein, we describe the validation of a new set of two-finger modules that can be used for building ZFAs via conventional assembly methods or a new strategy-finger stitching-that increases the diversity of genomic sequences targetable by ZFNs. Instead of assembling ZFAs based on units of the zinc-finger structural domain, our finger stitching method uses units that span the finger-finger interface to ensure compatibility of neighbouring recognition helices. We tested this approach by generating and characterizing eight ZFAs, and we found their DNA-binding specificities reflected the specificities of the component modules used in their construction. Four pairs of ZFNs incorporating these ZFAs generated targeted lesions in vivo, demonstrating that stitching yields ZFAs with robust recognition properties.

A conserved PTEN/FOXO pathway regulates neuronal morphology during C. elegans development.

The phosphatidylinositol 3-kinase (PI3K) signaling pathway is a conserved signal transduction cascade that is fundamental for the correct development of the nervous system. The major negative regulator of PI3K signaling is the lipid phosphatase DAF-18/PTEN, which can modulate PI3K pathway activity during neurodevelopment. Here, we identify a novel role for DAF-18 in promoting neurite outgrowth during development in Caenorhabditis elegans. We find that DAF-18 modulates the PI3K signaling pathway to activate DAF-16/FOXO and promote developmental neurite outgrowth. This activity of DAF-16 in promoting outgrowth is isoform-specific, being effected by the daf-16b isoform but not the daf-16a or daf-16d/f isoform. We also demonstrate that the capacity of DAF-16/FOXO in regulating neuron morphology is conserved in mammalian neurons. These data provide a novel mechanism by which the conserved PI3K signaling pathway regulates neuronal cell morphology during development through FOXO.

Inverted selective plane illumination microscopy (iSPIM) enables coupled cell identity lineaging and neurodevelopmental imaging in Caenorhabditis elegans.

The Caenorhabditis elegans embryo is a powerful model for studying neural development, but conventional imaging methods are either too slow or phototoxic to take full advantage of this system. To solve these problems, we developed an inverted selective plane illumination microscopy (iSPIM) module for noninvasive high-speed volumetric imaging of living samples. iSPIM is designed as a straightforward add-on to an inverted microscope, permitting conventional mounting of specimens and facilitating SPIM use by development and neurobiology laboratories. iSPIM offers a volumetric imaging rate 30× faster than currently used technologies, such as spinning-disk confocal microscopy, at comparable signal-to-noise ratio. This increased imaging speed allows us to continuously monitor the development of C, elegans embryos, scanning volumes every 2 s for the 14-h period of embryogenesis with no detectable phototoxicity. Collecting ∼25,000 volumes over the entirety of embryogenesis enabled in toto visualization of positions and identities of cell nuclei. By merging two-color iSPIM with automated lineaging techniques we realized two goals: (i) identification of neurons expressing the transcription factor CEH-10/Chx10 and (ii) visualization of their neurodevelopmental dynamics. We found that canal-associated neurons use somal translocation and amoeboid movement as they migrate to their final position in the embryo. We also visualized axon guidance and growth cone dynamics as neurons circumnavigate the nerve ring and reach their targets in the embryo. The high-speed volumetric imaging rate of iSPIM effectively eliminates motion blur from embryo movement inside the egg case, allowing characterization of dynamic neurodevelopmental events that were previously inaccessible.

Recognition models to predict DNA-binding specificities of homeodomain proteins.

MOTIVATION: Recognition models for protein-DNA interactions, which allow the prediction of specificity for a DNA-binding domain based only on its sequence or the alteration of specificity through rational design, have long been a goal of computational biology. There has been some progress in constructing useful models, especially for C(2)H(2) zinc finger proteins, but it remains a challenging problem with ample room for improvement. For most families of transcription factors the best available methods utilize k-nearest neighbor (KNN) algorithms to make specificity predictions based on the average of the specificities of the k most similar proteins with defined specificities. Homeodomain (HD) proteins are the second most abundant family of transcription factors, after zinc fingers, in most metazoan genomes, and as a consequence an effective recognition model for this family would facilitate predictive models of many transcriptional regulatory networks within these genomes. RESULTS: Using extensive experimental data, we have tested several machine learning approaches and find that both support vector machines and random forests (RFs) can produce recognition models for HD proteins that are significant improvements over KNN-based methods. Cross-validation analyses show that the resulting models are capable of predicting specificities with high accuracy. We have produced a web-based prediction tool, PreMoTF (Predicted Motifs for Transcription Factors) (http://stormo.wustl.edu/PreMoTF), for predicting position frequency matrices from protein sequence using a RF-based model.

A modified bacterial one-hybrid system yields improved quantitative models of transcription factor specificity.

We examine the use of high-throughput sequencing on binding sites recovered using a bacterial one-hybrid (B1H) system and find that improved models of transcription factor (TF) binding specificity can be obtained compared to standard methods of sequencing a small subset of the selected clones. We can obtain even more accurate binding models using a modified version of B1H selection method with constrained variation (CV-B1H). However, achieving these improved models using CV-B1H data required the development of a new method of analysis--GRaMS (Growth Rate Modeling of Specificity)--that estimates bacterial growth rates as a function of the quality of the recognition sequence. We benchmark these different methods of motif discovery using Zif268, a well-characterized C(2)H(2) zinc-finger TF on both a 28 bp randomized library for the standard B1H method and on 6 bp randomized library for the CV-B1H method for which 45 different experimental conditions were tested: five time points and three different IPTG and 3-AT concentrations. We find that GRaMS analysis is robust to the different experimental parameters whereas other analysis methods give widely varying results depending on the conditions of the experiment. Finally, we demonstrate that the CV-B1H assay can be performed in liquid media, which produces recognition models that are similar in quality to sequences recovered from selection on solid media.

FlyFactorSurvey: a database of Drosophila transcription factor binding specificities determined using the bacterial one-hybrid system.

FlyFactorSurvey (http://pgfe.umassmed.edu/TFDBS/) is a database of DNA binding specificities for Drosophila transcription factors (TFs) primarily determined using the bacterial one-hybrid system. The database provides community access to over 400 recognition motifs and position weight matrices for over 200 TFs, including many unpublished motifs. Search tools and flat file downloads are provided to retrieve binding site information (as sequences, matrices and sequence logos) for individual TFs, groups of TFs or for all TFs with characterized binding specificities. Linked analysis tools allow users to identify motifs within our database that share similarity to a query matrix or to view the distribution of occurrences of an individual motif throughout the Drosophila genome. Together, this database and its associated tools provide computational and experimental biologists with resources to predict interactions between Drosophila TFs and target cis-regulatory sequences.

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations to show that applying the trained 'de-aberration' networks outperforms alternative methods, and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.